1. Field of the Invention
This invention relates to predicting micro-topographic distribution of a terrain, and more particularly to a method for irrigating terrain comprising predicting micro-topographic distribution thereof. For any given field parameters, the method factors in both the randomness and the spatial dependence of the field relative to the elevation distribution when generating micro-topography spatial stochastic data.
2. Description of the Related Art
Field micro-terrain is the terrain undulation in relation to the field designed surface. Its spatial variability is determined by the various nodes on the field surface and their relative elevation spatial variability. A key parameter that quantifies micro-topography's spatial variability is the standard deviation Sd of relative elevation calculated from the field surface nodes. Standard deviation is a statistical parameter that measures dispersion between each node's relative elevation value and means calculated from all nodes' relative elevation values. Relative elevation is the elevation values of all nodes in relation to the field designed surface. The spatial variability of the surface micro-topography has significant impact to the surface irrigation flow movement. It is one of the key factors that affect the performance of a surface irrigation system.
The existing methodologies usually rely on field survey to collect surface relative elevation data, and then use numerical simulation to analyze and evaluate the impact of specific micro-topography spatial variability on field irrigation system's performance. However, using field survey method to collect surface relative elevation information is time consuming, costly and very difficult to get a wide range of relative elevation individuals. Its limitation in the numerical range and lack of spatial distinction negatively affect its flexibility and systemic when using simulation approaching to analyze and evaluate the relationship between micro-topography's spatial variability and filed irrigation performance. Therefore, it is necessary to develop a micro-topography distribution stochastic simulation methodology, which can produce a reliable and valid simulation result that can support the analysis and evaluation afterwards.
The probability distribution for filed surface relative elevation follows normal distribution. This means, when the statistical characteristics parameters (mean and standard deviation Sd) for field surface relative elevation is given, one can use Monte-Carlo simulation to generate relative elevation data of the along vertical slope, and then the micro-topography spatial variability information can be obtained. Such methodology is documented, e.g., in Transactions of ASAE, 1999, 42(4): P995-1008, “Assessing the potential for modern surface irrigation in Egypt”, as well as in “Research and application on new water-saving irrigation technique in the field” (China Agriculture Publishing House, 2002).
However, such method overlooks the transverse variability among the surface relative elevation data. It only factors in the randomness among the surface relative elevation data distribution, but overlooks its spatial dependence. As a result, the simulated data does not accurately reflect the actual spatial variability of the surface relative elevation. Also, the conventional method fails to account for the fact that theoretically more than one set of relative elevation data can be generated during the simulation if using the same set of statistical characteristics parameters. This poses new constraint on minimal sample size required for the simulation.
Assuming all possible field micro-topography distributions that meet a given set of statistical characteristics parameters as the total universe, and a single field micro-topography distribution as an individual, one must determine the minimum number of individuals needed for the simulation, so that the sampled individuals are representative to the whole universe. The number individuals that can represent the total universe are called sample size. Therefore, it is important to take both the randomness and spatial dependence of the field relative elevation distribution into the consideration, and develop new method to simulate two-dimensional surface micro-topography spatial variability, and solve the minimum sample size problem during the simulation process.